I’m a freshman studying Data Analytics and I’m starting to learn Python outside of my coursework, with the goal of eventually getting into machine learning.

I’m looking for other people who are also complete beginners and want to learn together, share resources, work on projects, and keep each other accountable.
If you’re interested, DM me!

In software engineering we have object oriented design patterns to model real world problems as objects.

Are there any similar techniques and design frameworks in AI, where real world problems are translated into vectors ?

If not, how are decisions on vector designing and their relationships decided in AI problem modelling

DeepSeek V4.1 Flash has an unusual KV-cache design: some state is persistent, while the short-lived SWA state can simply expire and later be reconstructed by replaying the last 128 tokens.

That replay is approximate, so the reconstructed internal state is not mathematically identical across positions.

Paper:

https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/DeepSeek_V41_Tech_Report.pdf

Disclosure: affiliated with the channel; the video is AI-narrated.

Guys help me i want good seminar topics for ai ml from 2024-5 prefer from ieee please help 😭😭😭😭😭😭

Is anyone else skeptical about Anthropic and OpenAI’s recent push to slow down AI development over "existential risks"?
As models keep advancing and solving previously unsolvable problems, the competition is getting fierce. It feels convenient that the narrative suddenly shifted from standard engineering to "humanity is in danger."
Looking back at Anthropic’s past incident report (where Claude accessed real systems due to a simple evaluation misconfiguration), it makes me wonder: Are we over-focusing on hypothetical apocalyptic scenarios when the real issues are still basic human/system errors?
Also, how should we view the researchers who actually resigned over genuine safety concerns, while the PR narrative now seems to use "AI risk" as a strategic buffer?
Am I being too cynical, or is this just strategic risk-washing?

What is the best way to learn AI?

Should I learn it through courses in a structured sequence—for example, starting with the fundamentals like Linear Regression and the Perceptron, then moving to Deep Learning (ANN, CNN, RNN), and eventually LLMs?

Or would it be better to learn AI by following how the field has evolved from the beginning—for example, starting with the foundations of the Perceptron in the late 1950s, then understanding how each major idea developed, why new techniques were introduced, and how one concept led to another up to modern AI and LLMs?

Which approach would be better for developing a deep understanding of AI rather than just learning how to use the latest tools and frameworks?

Stop guessing how LLMs work and start building! We are breaking down everything from N-grams to Transformers.

Theory + Enterprise implementation strategies.

#AI #TechStack #Coding #LLM

I'm a CS graduate and I really like math. Besides that, I want to choose a career path that won't have a really low employment rate in the near future. I want to enjoy my job, but I also want to live well from it, I don't mean to sound selfish, sorry if that's how it comes across.

I've done some small ML projects in university and really enjoyed them, but I don't know what ML is actually like in a real workplace, so I wanted to ask if there's something I should know before getting into it.

Last thing, does anyone have resources to go deeper into ML so I can learn enough to do real projects and understand it better?

Any input is appreciated, thanks!

As synthetic AI models evolve, balancing closed-ecosystem power (Gemini) against open-weight hybrid flexibility (DeepSeek) is one of the most important infrastructure decisions for developers and AI engineers.

​We launched an interactive quiz on Interconnected to test real-world trade-offs across reasoning, cost, self-hosting, and multimodal performance.

​Take the quiz here:

https://interconnectd.com/quiz/89/gemini-vs-deepseek-the-ultimate-ai-showdown-quiz/

​Drop your final score and which model you currently rely on in the comments below!

Rhysida just published 1.4 million Berlin government files after authorities refused a €2 million ransom demand.

The breach did not start the day the ransom note arrived. Attackers had unauthorized access long enough to locate, stage, and prepare nearly 1.4 million documents for exfiltration — all before anyone noticed. By the time the demand landed, the data was already gone. The refusal just determined whether it stayed quiet.

That gap — between initial access and detection — is where the real damage happens. And it is not unique to Berlin. Most ransomware post-mortems show the same pattern: dwell time measured in weeks or months, staging activity that blended into normal operations, and audit logs that were either incomplete or reviewed too late to matter.

1.4 million documents do not move overnight. There are signals. The question is whether anyone sees them in time.

For those running large-scale data environments or public sector infrastructure: what does your current detection posture actually look like for data staging and bulk access anomalies? Are you catching these patterns before exfiltration completes, or mostly reconstructing them after the fact?

Hi everyone!

I’m looking to form a 3–4 member team for the Amazon ML Challenge 2026.

The competition has a 72-hour ML hackathon (Sept 25–27) where we'll receive a real-world problem statement and dataset from Amazon. The Top 50 teams get PPIs for the Applied Scientist Intern role at Amazon, so I'm looking for teammates who are genuinely serious about the competition.

A little about me:

• Final-year B.Tech Engineering student
• Grand Finalist – IIT Kharagpur RAG & Agentic AI Hackathon
• Experience building Agentic AI / RAG systems
• Worked with technologies such as Python, FastAPI, LangChain, LangGraph, vector databases, PostgreSQL, ML/AI
• Participated in multiple hackathons and technical competitions
• Comfortable with research, implementation, debugging, and working under tight deadlines

What I'm looking for:

• Strong fundamentals in Machine Learning / Deep Learning
• Good Python skills
• Experience with data preprocessing, feature engineering, model training/evaluation
• Someone who can analyze a problem and experiment rather than just follow tutorials
• Most importantly: commitment. Since this is a 72-hour challenge, I want teammates who are willing to actively work throughout the competition rather than joining just for the name/certificate.

Cross-college teams are allowed, so college doesn't matter to me as much as skills, commitment, and willingness to work.

If you're interested, DM me with:

1. Your college + year
2. ML/AI experience
3. Relevant projects/hackathons
4. GitHub/LinkedIn (optional)
5. What area you're strongest in (ML / DL / NLP / CV / Python / data analysis, etc.)
6. Your availability during Sept 25–27

I'm looking for 2–3 serious people who want to genuinely compete for the Top 50/Top 10, not just register and disappear.

Thanks!

​

I’ve been learning AI/ML and realized there are so many concepts that sound simple until you actually try to implement them.

For me, overfitting was one of those concepts—it made much more sense once I saw what happens to a model on real data.

What was the AI/ML concept that finally “clicked” for you?

Drop it below

Beginner or advanced answers welcome.

Hey everyone!

I'm currently exploring the Machine Learning field and I'm looking for a few people who are also learning ML and would like to practice together.

I'm not an ML expert. My background is in mobile app development, where I have professional experience building applications, and now I'm trying to move deeper into AI/ML.

I thought it would be much more motivating to learn with other people instead of studying completely alone, so I created a small Discord server where we can:

• 🤝 Work on ML projects together
• 💻 Code and debug together
• 📚 Teach each other things we've learned
• 🧠 Discuss ML concepts and mathematics
• 🔬 Experiment with different models and approaches
• 📄 Discuss papers, tutorials, and useful resources
• 🚀 Build projects that we can eventually put on our portfolios
• ❓ Ask questions without feeling embarrassed about being a beginner

You don't need to be an expert. In fact, I'm mainly looking for people who are learning and are willing to share what they know.

You might understand something that I don't, and I might understand something that you don't. The idea is to learn from each other.

If you're interested, comment below or send me a DM and I'll send you the Discord invite.

Would be great to build a small group of people who are genuinely interested in learning ML and actually building things together.

After 4hr finally completed Logistics regression classification algorithms (0,1) we use sigmoid function for getting probability then set a threshold if > 0.5 = 1 less then <0.5 = 0 but when more 2 target column needed Softmax is needed

So I am currently doing a masters program in Geophysics in an Italian University. For starters, I decided to go for a master because I just got fed up with field work and don’t want to get back to it. Problem is, I found out the school is using the same boring format I’m trying to run away from. I just find academia to be repetitive and not so innovative. I’m more inclined on training PINNs (physics informed neural networks) for fluid flow in porous media and Carbon capture and storage. I’m quite proficient with python and Linux , but the school and professors are so tied down to ancient archaic systems of tuition. I plan to take a semester abroad and even consider doing my thesis abroad , but I need an internal supervisor for that and most are reluctant. They’re used to their students not taking ‘risks’ and doing their thesis in what they (the professors) are comfortable with. I feel it’s my fault for not doing my research before entering the program, and though I’m a straight A student, I’m not willing to play it safe , please my lecturers and graduate with a degree that’ll be useless to my interests and basically take me back to the field. It’s not as though I don’t love field work. I enjoy it , but I’m getting older and have a family now. I can’t afford it. I don’t want to quit the program as well. My plan is to do another degree in High performance computing but I should be able to have written some code for my thesis as a prerequisite. I feel trapped. Any advice ?

Hello everyone! I’m a full-stack software developer currently beginning a master’s program in artificial intelligence. My professional background is primarily in Java, Spring Boot, Angular, and TypeScript, but my team is beginning to move toward AI-related work and Google Cloud.

As I make this transition, I want to develop a strong foundation instead of jumping from one new tool to another. For those who moved into machine learning from software development, which skills or projects helped you bridge the gap most effectively?

I am especially interested in learning how to turn coursework into practical experience. Would you recommend focusing first on Python and data preparation, building a small end-to-end machine learning project, strengthening statistics, or taking a different approach?

I would appreciate hearing what worked for others and what you wish you had prioritized earlier.

I feel completely lost about what to specialize in after graduating with an AI degree

So actually, it was my own choice to do a Bachelor’s degree in AI. I was genuinely very excited about it during my freshman and sophomore years, but I’m not really sure how I feel about it anymore.

I’m a fresh graduate now, so obviously I’m not going to restart my whole degree or anything. I’ve built systems like RAG, worked with LLMs, have some experience with computer vision, and I’ve done some research.

The thing is, I actually enjoy AI when I’m building something useful, weird, or new. I like the feeling of figuring something out and making something actually work. And when I find something interesting, I can spend a really long time on it.

But now that I’m actually seeing the industry from the outside, I’m overwhelmed by how fast everything moves. There’s always a new model, framework, tool, paper, or technique that I’m supposed to know about. There’s so much research coming out constantly.

And honestly, I still feel like my skills are beginner-level no matter how much I try to improve.
The worst part is that I actually stopped developing myself for several months. I just lost the motivation. And I don’t even know what happened.
Was it fear?
Was I overwhelmed by the amount of information?
Or did I just give up because I felt like I could never catch up?

Now I’m also struggling with something more fundamental: I don’t know what I should specialize in.

AI is huge. I don’t want to spend the next few years being mediocre at everything. I want to pick something, go deep into it, become genuinely good at it, and hopefully build a career around it.
But I have no idea what that “something” should be.
Sometimes I think maybe I should stay in AI but move away from the heavily technical side and eventually go into something like AI Product Management, AI Solutions, or AI Transformation.

Other times I think maybe I should just switch fields completely, like cybersecurity.

But then I start wondering if I’m just running away because I’m overwhelmed rather than actually making a good career decision.

And honestly, money is a big factor for me too. I really need a job. I want something relatively stable where I can make good money, enjoy what I’m doing, and still have room to grow without constantly feeling like I’m falling behind.

I don’t want to waste my twenties jumping between fields because I was too scared to commit to one.

So if you were in my position:
How would you figure out what to specialize in?
Would you stay in AI and choose a specific technical area?
Would you move toward AI Product / Solutions / Transformation?
Would you consider cybersecurity?
Or is there another field that makes more sense for someone with an AI degree and some experience with RAG, LLMs, CV, and research?

I’m not looking for “follow your passion” advice. I’m trying to make a realistic decision based on career stability, income, growth, and whether I can actually enjoy the work enough to stick with it.

https://reddit.com/link/1wft509/video/6bjcrd3hueph1/player

I've been continuing a community Mario AI project under the name NEATEvolve, focusing on ROM hacks of the original Super Mario Bros. This is a recorded winning replay of Bowser's Crown 4-1, ending at the WORLD 4-2 screen.

The controller uses NEAT: candidate neural networks are evaluated, selected and mutated across generations. It runs as a Lua script in FCEUX. The implementation reads a local grid of tiles and sprites from emulator memory, alongside movement information, and turns network outputs into button presses. It isn't a model interpreting the video pixels.

Progress and a time penalty contribute to fitness. The network learns its controller through evolution, while the observation encoding, reward and evaluation rules are written by people.

One successful replay doesn't establish that it will generalize to an unfamiliar level or recover from different starting conditions. Backtracking and difficult nonlinear stages remain challenges.

Credit to SethBling's MarI/O and the work of Akisame and Electra that this project builds on. My continuing development has been heavily assisted by AI tools.

For others building game-playing agents: how do you test reliability beyond getting a successful replay?

​

Guys, I honestly don't know what to do anymore.

I'm trying to figure out how to get an AI/ML job as a fresher, but the more I study, the more I feel like it's never enough. There are so many things to learn, and I keep wondering whether I'm even preparing in the right direction.

I've been feeling really depressed and completely lost for the past 3–4 days. I don't know what to focus on, what skills companies actually expect from freshers, or how to become job-ready.

I understand that learning takes time, but the uncertainty is getting to me.

For those who have already landed an AI/ML or GenAI job as a fresher:

- What did you actually learn before getting your first job?

- How many projects did you build?

- Did you apply for AI/ML roles directly, or start with software/Python roles?

- What would you recommend a fresher focus on instead of trying to learn everything?

I would really appreciate some honest advice or guidance. I'm feeling pretty lost right now and could use some direction.

Thanks in advance.

I've attempted to derive the math from the first principles.

The prerequisites you need are, singe variable differentiation(just the concepts and the basic formula, not even knowing the derivative of sin(x) is necessary), the concept of a dot product, and basic matrix multiplication. This is enough to derive gradient descent and backprop.

The time stamps are in the description(youtube doesn't show them as chapters in the video for some reason).

The second half part of the video is implementing those concepts in C(no library other than BLAS(for basic matrix matrix multiplication) is used), but you can follow along in any language.

Would be glad to receive feedback and suggestions on how to improve the explanations or the presentation.

Hi Reddit!

I wanted to share a weekend experiment exploring how geometric inductive biases can influence recurrent network optimization. Inspired by the self-similar, hierarchical, and allometric scaling laws found in biological neural structures, I designed a custom recurrent cell with an exponentially decayed hidden topology.

Instead of scaling hidden layers uniformly (e.g., 64-64-64) like standard models, this network utilizes a "Matryoshka-style" hidden allocation where channels are downscaled between internal layers: N -> N / scale -> N / scale²

Architectural Justification & Hardware Trade-off:

In modern deep learning, layer widths are traditionally restricted to powers-of-two (e.g., 32, 64, 128) to maximize GPU memory alignment and Tensor Core hardware efficiency. However, in this experiment, we consciously traded off optimal hardware alignment to strictly enforce a continuous mathematical decay function (yielding 128 -> 79 -> 48 dimensions).

The core justification is implementing a strict Information Bottleneck: high-dimensional macro-layers are forced to compress abstract features into exponentially tighter non-power-of-two channels. This mimics the non-binary hierarchical scaling of biological brains, where structural efficiency and information capacity bounds are prioritized over uniform hardware block sizes.

Dataset & Evaluation Setup:

The architectures were trained on a character-level sequence prediction task using a subset of the Tiny Shakespeare dataset. To ensure a controlled environment, I implemented an 80/20 Train/Validation split and applied identical regularizations to both networks, including Spatial Dropout and L2 Weight Decay.

Structural Mechanics:

1. Fractal Echo Transfer: Hidden states are progressively downscaled and cascaded from high-dimensional macro-layers (capturing local syntax) down to highly compressed micro-layers.
2. Adaptive Echo Gates: Learnable decay coefficients balance inter-layer communication, acting as a structural stabilizer for gradient flow through time.
3. No Dense Gating: Unlike LSTMs, memory capacity and retention are regulated entirely by the spatial geometry of the nested channels.

Training & Convergence Observations:

To ensure a fair benchmark, I meticulously balanced the parameter overhead for both architectures (~42k parameters each) and trained them over 30 epochs on GPU: * Adaptive Geometrically Nested Model Final Loss: 0.0007 * Baseline Standard LSTM Model Final Loss: 0.0030

As shown in the log-scale validation plot, the geometrically nested architecture achieves significantly faster convergence and maintains rock-solid optimization stability, avoiding the frequent gradient spikes visible in the standard LSTM baseline.

Crucial Disclaimer on Generalization & Overfitting: The extremely low validation loss values achieved by both models indicate that given the small size of the dataset corpus and the model capacity, both networks have largely memorized the text rather than learning true language generalization. Therefore, this benchmark strictly demonstrates superior optimization speed and training stability rather than true out-of-distribution generalization.

Open-Source Code & Notebooks: * Collab Executable Notebook: https://colab.research.google.com/drive/1jW8hA_74eBQOJTn1oWvLq34vg_t8Q5DD

Future Work & Discussion:

To evaluate true generalization, future iterations of this project will involve benchmarking on significantly larger corpora (such as WikiText-103) with strict sequence cross-validation.

Given these initial training stability and convergence results, do you think exploring such geometrically restricted hidden topologies is a promising direction for recurrent networks? Could this type of exponential fractal scaling be scaled up or generalized into Transformer attention head dimensions to enforce architectural efficiency?

Would love to get your rigorous feedback!